Non-alcoholic fatty liver disease (NAFLD) is very common, with estimates that it affects 30% of the United States' population. Although NAFLD has been clearly linked to metabolic syndrome, no specific targeted therapies are currently available. As a result, this disease often progresses to cirrhosis and liver failure. Mitochondria are the major cellular source of reactive oxygen species (ROS) in NAFLD, yet very little is known about the life cycle of hepatic mitochondria and its role in this disease. Our long-term goal is to combine mathematical modeling with high-throughput proteomic, genomic and metabolomic approaches to determine how accelerated mitochondrial ROS production leads to mitochondrial dysfunction in NAFLD. Our central hypothesis is that the accelerated ROS production causes enhanced degradation of electron transport chain (ETC) and ATPase subunits. Our studies of mitochondrial dynamics using dynamic proteomics will shed new light on the mechanisms underlying oxidative stress in NAFLD through the following three specific aims, to: 1) develop the methodology and software for global 2H2O-proteome dynamic studies in vivo, using Bayesian modeling to avoid overfitting of isotopomers to the experimental isotope profiles; 2) determine protein turnover rates using a novel nonparametric, data-driven stochastic model; and 3) apply the newly developed bioinformatic tools to determine the influence of high fat diet-induced hepatic oxidative stress on the synthesis of ETC and ATPase subunits in a mouse model of NAFLD by evaluating mitochondrial respiratory function, oxidative stress, oxidative mtDNA damage, and proteome expression and dynamics. Once it is known how proteins involved in the ETC and ATP synthesis are affected by ROS, it should ultimately be possible to modulate their expression levels pharmacologically, resulting in new and innovative approaches to the prevention and treatment of NAFLD. This project will produce novel bioinformatic tools for quantifying protein turnover rates, including a model of protein networks i heavy water (2H2O)-proteome dynamics that are fundamentally statistical and will be applicable to different labeling strategies. The use of 2H2O for metabolic labeling has several advantages, including safety, increased sensitivity (due to the incorporation of multiple copies of 2H into the analyzed peptides) and lower cost. Liquid chromatography coupled to mass spectrometry of metabolic labeling over a time course will provide quantitative information about the relative incorporation levels of different isotopes. The development of new bioinformatic methods for stochastic modeling of the turnover rates, and de- convolving isotope profiles of co-eluting species, will permit large-scale, automated evaluation of the protein turnover rates. As a result, this work will significantly advance and expand dynamic proteome studies quantifying protein decay and synthesis in vivo. This can then be used to investigate such other diseases as diabetes and neurological disorders.